Abstract
Techniques for general purpose optimization have been derived from the Metropolis Monte Carlo method of simulating the behavior of particles in substances as they are slowly cooled to form crystals. Simulated Annealing is such a derivative and its value for placement problems (e.g., in circuit board layout design) suggests that it could be advantageously applied to clustering tuples in databases in order to enhance responsiveness to queries. In this article we investigate this issue and compare the performance of this technique with a Graph-Collapsing clustering method which is known to perform very well, in order to gain insights into which approach is better for incorporation in a performance-oriented database design tool. We judge that, whilst the new method does give superior results to the graph-based method in many cases, these improvements are gained at such a very considerable expense of algorithm run time as to rule the new technique out of our consideration as a real world general purpose design tool (but perhaps not for some special-purpose databases). © 1990 John Wiley & Sons, Inc.
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